The effect of aggressive group norms on young adults’ conformity behavior in WhatsApp chats: a vignette-based experiment

Social networking and messaging applications, such as WhatsApp, have become an essential social environment for adolescents and young adults (AYA). While facilitating connectivity, they also bear hazards, including cyber-aggression. This study investigates the impact of (aggressive) group norms on AYA's propensity to expect cyberaggressive behaviors within different group chats. Based on a vignette scenario, realistically simulated WhatsApp group chats enabled scrutinizing, if and how exemplary reactions (funny, aggressive, friendly) of group members influence AYA's conformity to cyber-aggression (N = 500, aged 16 to 29). Additionally, we examined the effect of chat group type—close friends versus fellow students—on the anticipation of aggressive reactions. Sociodemographic, social, and developmental-psychological factors were evaluated for potential effects. Multilevel logistic regression analyses indicated that aggressive group norms significantly predict cyber-aggression anticipation, while no effect of chat group type was observed. Controlling for the size and vivacity of participant’s friend group, gender, age, and educational status were significant predictors: males, younger participants, and non-university students expected higher levels of cyber-aggression conformity. This study underlines the importance of group dynamics on perceptions of cyber-aggression and hints at individual risk factors for AYA's digital communication behavior.


Sample
We recruited 553 initial participants through various digital channels (mailing lists, social media) and direct contacts at German educational institutions.Referring to AYA 36 , we excluded participants over 29 years of age and those who did not pass the control items (n excluded = 53, 9.6%).The final sample consisted of N = 500 individuals (398 females, 99 males, 3 no indication, 0 non-binary) with a mean age of M = 22.28 years (SD = 2.55, Range = 16-29 years).Most participants were university students (80.0%), followed by those in the labor force (9.8%), apprentices (7.8%) and school students (2.4%).Educational attainment was high, with most having A-Levels (67.8%) or university degrees (22.4%).
All participants gave their informed consent prior to data collection.Participation was voluntary, and participants could win vouchers or earn course credits (university students of RWTH Aachen University and HSD Hochschule Döpfer) upon completion of the study.

Procedure and experimental manipulation
The study was conducted online from July to November 2022.Participants first answered introductory questions about their sociodemographic data and daily usage of the Internet, social media, and instant messaging apps.Without prior disclosure of the research topic, participants were then presented with image-based vignette scenarios (experimental manipulation) and questionnaires (concomitant variables) to assess conformity to cyberaggression in WhatsApp group chats.
We created six image-based vignettes by varying features of a group chat depicting a potential visual and written-verbal cyber-aggression scenario 8 .Participants were randomly assigned to either a friends' or a fellow students' WhatsApp chat condition.Both groups viewed three fictional group chat screenshots showing a pixelated individual's photo (see Fig. 1).The chats depicted three group norms: (1) funny/mocking, (2) insulting/ aggressive, and (3) compassionate/friendly, with manipulated reaction tones and forms (emoticons, comments) while keeping other characteristics constant.Gender-neutral names were used for the group members, and the presentation order was randomized to minimize confounding or sequencing effects.
Participants were instructed to imagine that the shared image depicted an external person in an unfavorable pose without consent, using a schematic representation for ethical reasons (see Fig. 1).They completed two tasks: estimating the number of close friends they contact via social media (control variable) and estimating how many of those friends would respond with specific reactions (funny, aggressive, or friendly) for each chat scenario.
After completing these tasks, participants answered questionnaires assessing personal characteristics, including developmental task fulfillment and importance (26 items extended version) 51 , ingroup prototypicality, and ingroup identification (2 items) 5,52,53 .To ensure attentive participation, two control items were included.Participants could optionally provide feedback on the study.The debriefing phase provided information about the study's purpose.The study took approximately 30 min to administer.Data collection was executed via PsychoPy, enhanced with HTML and JavaScript, and hosted on Pavlovia (https:// pavlo via.org/).

Outcome variable 'cyber-aggression'
We operationalized cyber-aggression on WhatsApp as expected conformity to aggressive group norms (reactions in the chat history).To ask respondents directly about their own ' cyber-aggressive' behavior is subject to strong www.nature.com/scientificreports/effects of social desirability.At least, researchers of a survey like ours would be perceived as protagonists of a norm against offensive Internet behavior.Answers can therefore be expected to be edited in favor of a societal norm of 'netiquette' in some way.On the contrary, in case our respondents share a self-categorization as 'bad boy'/'bad girl' for some reason (not uncommon among AYA), reactions might be biased and exaggerated in a direction of social deviance.We therefore used an indirect measure (similar to the contrast vignette technique of Burstin et al. 54 ) by presenting a specific situation and then asking the respondents to estimate the number of their close friends who they expect to behave in a certain way.The instructions provided were as follows (English translation): After indicating the number of close friends, participants were asked to estimate the number of their close friends who would react to the messages in any of the following nine ways: (1) forward the photo without consent, (2) send a laughing emoji (face that laughs tears), (3) write a 'funny' comment, (4) send an emoji expressing disgust/aversion (nauseated face), (5) write an insulting/aggressive comment, (6) send an emoji expressing to be shocked (face with open eyes and hand over mouth), (7) write a comment expressing compassion, (8) write a comment calling for the photo to be deleted, (9) show a different reaction (open input).Response options (1), (4) and (5) were classified as cyber-aggressive reactions.Multiple answers for each reaction were possible but not mandatory.Responses indicating any friends exhibiting aggressive reactions were subsequently simplified and categorized as 'cyber-aggression expected' vs. 'no cyber-aggression expected' .

Concomitant variables/additional measures
Sociodemographic variables and Internet use were measured as potential influencing variables.The participants' gender (female, male, non-binary, no indication), age, and educational status (high school, university, work, etc.) were queried.Participants were asked to report the number of hours they spend online daily for personal purposes, such as communicating with friends or entertainment, and for educational or work-related activities, such as attending online meetings or researching information for assignments.nd literature research, the questionnaire was adapted to the current state of the art and the reality of AYAs' lives nowadays 46,42 .The resulting scales reached a Cronbach's alpha of α = 0.57 (fulfillment) and α = 0.61 (importance), indicating only limited reliability.
A single item was used to measure self-perceived ingroup prototypicality among friends who communicated through instant messages, email, and social networks (see Piccoli et al. 5 and Jetten et al. 49 ).Participants were asked to rate their typicality within the group on a 4-point scale from 1 'not typical at all' to 4 'very typical'.Higher scores on this scale indicate a higher level of perceived typicality within the group.
Cognitive identification with the ingroup was measured using the single item 'Inclusion of the Ingroup in the Self (IIS)' (7 point scale, no overlap to almost complete overlap) proposed by Tropp and Wright 53 and also used by Piccoli et al. 5 .Details of construction and application can be found there.Higher scores on this scale indicate a greater level of closeness with friends who are in contact through smartphones and the Internet.
In addition, two control variables were measured to account for potential confounding influences related to the operationalization of cyber-aggression.The first control variable was 'number of close friends' , measured by asking participants to estimate the number of close friends they contact via smartphone or social networks.
The second control variable was ' expected group vivacity' , measured by the total number of expected reactions from close friends for each scenario.Group vivacity reflects the collective energy, enthusiasm, and level of interaction within the group and can be operationalized through indicators such as the frequency and intensity of interactions or the presence of discussions.Controlling for the size of one's close friend group and the general expected liveliness of WhatsApp group chats was critical, as both factors may influence AYA's estimates of the number of their friends who would potentially engage in cyber-aggression.
All items were originally presented in German and have been translated into English for the purposes of this paper.

Design and statistical analyses
The experiment used a 2 × 3 factorial design with type of group chat (friend group vs. fellow student group) as a between-subjects factor and displayed group norm (funny/mocking, insulting/aggressive, compassionate/friendly) as a within-subject factor.The dependent variable was expected cyber-aggressive reaction (0/1).For reasons of simplicity, and because answers were given in a clear zero-inflated distribution (a considerable part of participants would not expect any of their friends to behave aggressive), the dependent variable of the experiment was summed up over the three indicative options as a dichotomized variable 'cyber-aggression expected as present/ not present' (1/0) in the defined type of group (see above for detailed information).
During the confirmatory regression analyses (multilevel logistic regression), we initially tested the experimentally varied factors: type of group chat (level 2) and displayed group norm (level 1).This stage focused on establishing the causal effects of the experimental manipulations without the influence of additional variables.The confirmatory model aimed to provide clear, causally interpretable results regarding the hypotheses H 1 and H 2 of our study.
Following this, we adopted a hierarchical regression approach to build a comprehensive model incorporating various concomitant variables [56][57][58] .We dichotomized all concomitant variables into 0/1 categories based on median splits to simplify the analysis and enhance interpretability (see Table 1 for median values and Table 3 for references) 59 .The hierarchical model was constructed in several stages, each adding a new set of variables to the confirmatory model to assess their potential impact and improve model fit.The order in which the exploratory www.nature.com/scientificreports/factors were added was based on a theory-driven approach 57,58 , incorporating predictors that were already presumed to yield anticipated outcomes first and more exploratory ones last.In the first step, we added the number of close friends and group vivacity as control variables to hold them constant.Next, we added sociodemographic variables (age, gender, education level), social identification variables (ingroup prototypicality, and ingroup identification), intensity of Internet usage (personal and work-related), and developmental task fulfillment and importance.
Model improvement was subsequently tested by comparing log-likelihood ratios, Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) between each step.Finally, a parsimonious prediction model was computed, retaining only those parameters which inclusion in the model significantly improved the model fit.This three-step approach, beginning with a confirmatory model, followed by a hierarchical inclusion of concomitant variables, and culminating in a refined final model, allowed us to systematically examine the contribution of various factors to friends' predicted cyber-aggression.The statistical analyses were performed using R 60 and RStudio (Version: 2022.12.10) 61 , especially the lme4 package (v1.1.27.1) 62 .Significance tests were performed at α = 0.05.

Preparatory analyses
Descriptive analyses showed that all participants used instant messaging applications, with WhatsApp being the most popular (97.8%).Participants spent an average of four hours per day online for personal use (M = 3.84, SD = 1.77) and for work/education (M = 4.04, SD = 2.21).On average, they had completed or were completing 9 out of 13 developmental tasks, with 12 tasks rated as somewhat or very important (see Table 1).Participants reported having an average of ten close friends (M = 9.66, SD = 7.48) and estimated 12 reactions (M = 11.75,SD = 13.48) from these friends as group vivacity (e.g., six comments, six emoticons).The median values of the predictor variables are shown in Table 1.
Of the 500 participants, 259 were assigned to the friend group condition (51.8%) and 241 to the fellow student group condition (48.2%).Randomization checks revealed no significant differences between groups in terms of age, gender, education level, Internet use, developmental tasks, number of close friends, or expected group vivacity (p > 0.05).However, participants in the friend group condition spent significantly less time online in their free time than those in the fellow student group condition (36 min less per day; t(498) = 4.45, p < 0.05, Cohen's d = 0.23).

Multilevel logistic regression
The chance for cyber-aggression reactions (log-odds) was quantified through multilevel logistic regression analysis.The intra-class correlation coefficient (ICC) indicated that approximately 76% of the variance in cyberaggression can be attributed to inter-individual differences.Out of all 1,500 trials reported, 672 (44.7%) could be rated as cyber-aggressive.This means that in 44.7% of all observations (level 1), participants expected at least one of their friends to engage in some form of cyber-aggression (e.g., aggressive comment/emoticon).Taking repeated measures into account, this means that 301 participants (59.9%) anticipated some form of aggressive reaction in at least one of the given scenarios at the subject level (level 2).

Confirmatory analysis of cyber-aggressive reactions
We conducted a confirmatory analysis using multilevel logistic regression, clustered by participant (level 2), and predicted by group chat type (level 2) and group norm (level 1).Results from Table 2 indicate that an aggressive group norm in the chat significantly predicted cyber-aggression expectations.Participants anticipated a greater amount of cyber-aggressive reactions when aggressive messages were already displayed (β = 0.54, OR = 1.72, 95% CI [1.16-2.54],p < 0.001).The odds of estimating an aggressive response increased by 72% in the presence of a cyber-aggressive norm, compared to a friendly or funny norm.Conversely, the implementation of a funny group norm did not result in a significant increase in cyber-aggression (ß = 0.21, 95% CI [0.84-1.83],p = 0.28).Contrary to our initial hypothesis, we did not observe an effect of group chat type (friends vs. fellow students) on cyber-aggression (ß = − 0.40, 95% CI [0.33-0.98],p = 0.67).www.nature.com/scientificreports/

Exploratory analysis including concomitant variables
The initial step of the hierarchical multiple logistic regression was conducted to determine the influence of the control variables, number of close friends and group vivacity, on the prediction of cyber-aggression.This model showed a significantly better fit than the confirmatory model, χ 2 (2) = 125.27,p < 0.001, ΔAIC = − 121.3.In a second step, sociodemographic variables (age, gender, education level) were added to the model.This addition significantly improved the model fit, χ 2 (3) = 34.50,p < 0.001, ΔAIC = − 28.5.The third step included social identification variables (ingroup prototypicality, ingroup identification).Although this step did not significantly improve the model, χ 2 (2) = 4.64, p = 0.10, ΔAIC = − 0.6, it slightly increased the marginal R 2 to 0.250 and the conditional R 2 to 0.805.In the fourth step, variables for the intensity of Internet use were included.This step did not significantly enhance the model, χ 2 (2) = 0.17, p = 0.92, ΔAIC = + 3.8.In the final step, adding the AYA developmental task fulfillment and importance variables, did not significantly improve predictive performance, χ 2 (2) = 0.96, p = 0.62, ΔAIC = + 3.1.In conclusion, the hierarchical model-building process identified key predictors of cyber-aggression that contributed to the model's predictive performance, as detailed in Table 3 and Fig. 2.

Final parsimonious model
The final parsimonious regression model, which was built using only variable sets from the exploratory hierarchical model that significantly improved the model's predictive performance, revealed several relationships between the studied predictors and cyber-aggressive reactions (see Table 4).The intercept of the model indicated a baseline likelihood of cyber-aggression when all predictors were at their reference levels (OR = 0.81, 95% CI [0.25, 2.66], p = 0.73).Comparing friend groups to fellow student groups did not significantly predict aggressive reactions (OR = 0.69, 95% CI [0.34, 1.37], p = 0.28), suggesting that group chat type alone does not influence the propensity for cyber-aggression in the present study.Group norms were found to be a significant factor, with aggressive norms increasing the likelihood of cyber-aggression by 1.82 compared to friendly or funny norms (OR = 1.82, 95% CI [1.21, 2.73], p < 0.01).The likelihood of cyber-aggression was found to be higher for individuals with more than seven close friends (OR = 2.82, 95% CI [1.30, 6.11], p < 0.01).A high level of group vivacity, as indicated by anticipating more than eight WhatsApp group reactions per chat, was found to be significantly associated with cyber-aggression (OR = 19.56,95% CI [9.74, 39.27], p < 0.001).After controlling for both control variables, it was found that individuals under the age of 21 were significantly more likely to anticipate cyber-aggression from their friends than those aged 21 to 29 (OR = 3.33, 95% CI [1.51, 7.36], p < 0.01).Female respondents were 0.12 times less likely Table 3. Hierarchical multiple logistic regression of expected cyber-aggressive reactions as predicted by type of group chat and group norm.Predictors in steps 1-4 have been omitted for the sake of clarity.Model improvement was tested by comparing the log-likelihood ratios between each step.Reference categories: a fellow student group, b friendly group norm, c 0-7 close friends, d lower anticipated vivacity (> 8 group reactions), e 21-29 years old, f male respondents, g all other educational level, h ingroup prototypicality low, i ingroup identification low, j lower level of Internet usage (median or below), k lower fulfillment of developmental tasks (median or below), l lower importance of developmental tasks (median or below).than males to anticipate cyber-aggression in their WhatsApp groups (OR = 0.12, 95% CI [0.05, 0.30], p < 0.001).Furthermore, university students showed a negative association with expecting cyber-aggression compared to respondents with other educational statuses (OR = 0.32, 95% CI [0.13, 0.77], p < 0.05).

Model
The final model's marginal R 2 of 0.243 and conditional R 2 of 0.803 indicate that the predictors accounted for a substantial proportion of the variance in aggressive reactions.A likelihood-ratio test showed a significant improvement in model fit after the variable sets were added (χ 2 (5) = 159.77,p < 0.0001).The AIC and BIC decreased from 1649.5 to 1499.7 and from 1676.1 to 1552.9, respectively, indicating an improved fit.The full model, including additional predictors, did not improve model fit (χ 2 (6) = 5.77, p = 0.45) and had higher AIC and BIC values (1506.0 and 1591.0).This suggests that the final model, including the control and concomitant variables of interest, not only fits the data better than the confirmatory model, but is also more parsimonious.3.

Table 4.
Final parsimonious logistic regression model for cyber-aggressive reactions as predicted by type of group chat and group norm.Predictive performance and parsimony of the final model were preferred over the statistical significance of individual predictors.Reference categories: a fellow student group, b friendly group norm, c 0-7 close friends, d lower anticipated vivacity (> 8 group reactions), e 21-29 years old, f male respondents, g all other educational level, h ingroup prototypicality low, i ingroup identification low, j lower level of Internet usage (median or below), k lower fulfillment of developmental tasks (median or below), l lower importance of developmental tasks (median or below).www.nature.com/scientificreports/

Discussion
Our study explored expected cyber-aggressive reactions of 16-29-year-olds in WhatsApp group chats when responding to an offensive photo shared without consent, and examined whether induced group norms-funny, aggressive, or friendly-influenced expected conformity to aggression.We also manipulated and evaluated chat group type (close friends versus fellow students).To examine the association between sociodemographic variables, social identification variables, developmental tasks, and cyber-aggression and to control for the size and vivacity of friend groups, we conducted an exploratory and extended version of the confirmatory analysis model.Our results therefore provide some insight into the influence of groups on cognition and emotion.
In line with our first hypothesis, the induction of different group norms (fictitious first reactions in the respective chat, see Fig. 1) affected the conformity to cyber-aggression as tolerable behavior: AYA expected significantly more aggressive reactions from close peers in their group chats when aggressive reactions were already present.This effect remained stable even after adjusting for confounding variables (see Tables 3 and 4).The likelihood of cyber-aggressive responses was almost twice as high as in the other group norm conditions (friendly, funny).This result supports the findings of other researchers 5,19 who examined cyber-aggression in early adolescents and found a positive association between social identification and group conformity using observational approaches.We extend their findings by confirming the causal influence of induced group norms on the expected behavior of AYA in an experimental study.In the WhatsApp study that forms the basis of our approach, Bleize et al. first tested causal influences 9 , only finding a direct effect of accountability on conformity to aggressive norms in two experiments.Using a novel approach to simulate WhatsApp group chats, we were able to show that group norms-even experimentally induced ones-in the form of different interaction tones and reactions can have an impact on (the anticipation of) subsequent aggressive group behavior.This finding supports the view that cyberaggression is highly peer-influenced 63,64 and thus likely an age-, development-, and platform-related 2,4 behavior.
Contrary to our second hypothesis, assigning respondents to different types of group chats, did not affect cyber-aggression conformity.Given our base rate of approximately 45% aggressive reactions and testing with a type I error risk of 0.05, our sample size allows us to detect an odds ratio of ≥ 1.35 with power > 0.8.This means that 'type of group chat' either has a smaller effect size or has no effect on the respondents' cyber-aggressive tendencies (post hoc power calculations with G*Power 3.1) 65 .The latter explanation is supported by the fact that group type had no effect in the previously cited study by Bleize et al. 9 .The researchers manipulated group centrality, but this also failed to show a direct effect.However, both findings are inconsistent with what the Social Identity (SI) perspective 17,18 and other previously proposed models 5,49,50 predict.One possible interpretation could be that peer influence is so dominant among AYA in the WhatsApp environment that it suppresses the effect of different group importance.This may be particularly applicable for an AYA sample that tends to be highly susceptible to social influences in general 39,40 .Slagter et al. 50found that adolescents preferred certain attributes such as 'coolness' , 'acting mean' , and 'higher peer status' as a source of information within their social networks.Although beyond the focus of our study, future research could examine whether participants ascribed these traits to peers in the presented groups.If the non-significant 'friends vs. fellow students' result is the consequence of a relatively weak experimental manipulation, future studies could test a stronger one.For example, presenting WhatsApp group photos only to the friend group might contribute to a stronger differentiation between friends and fellow students and thus induce the expected effects.For now, it is assumed that individuals respond to group norms regardless of the strength of their social identification in the context of WhatsApp groups.
The predictive power of the statistical model was improved by the inclusion of potential confounding variables: age group, gender, educational status, as well as the control variables 'number of close friends' and 'group vivacity' .However, due to their observational nature, they cannot be interpreted causally 66 .The successful inclusion of these variables reflects the existing literature, suggesting a substantial impact of demographic characteristics on cyber-aggression.First, the pronounced expectation of cyber-aggression among respondents under the age of 21, which is three times higher than that of their 21-29-year-old counterparts, aligns with studies indicating that emerging adults are more immersed in digital interactions, potentially increasing exposure to and engagement in cyber-aggression 26,32,34 .
Second, female participants were less likely to anticipate cyber-aggressive responses and exhibited less conformity in our experimental settings compared to males.This finding is consistent with other research on cyberbullying perpetration 5,25,28,67 .Therefore, gender differences can be explained by an interdisciplinary framework that incorporates gender role socialization theory 68,69 and empirical research insights 53,70,71 .Gender role socialization theory explains that societal conditioning and deeply ingrained gender stereotypes result in different behavioral expectations and norms.During adolescence, males may be more likely to engage in deviant behaviors to gain peer approval.This tendency may escalate as gender roles become more pronounced and such behaviors are viewed as pathways to elevated status among male peers.In contrast, females are often associated with stereotypes of nurturance and emotional expressiveness, and therefore may be less likely to engage in such behaviors.Identity theory 72 also supports this idea by highlighting the role of parental expectations in fostering traits such as prosocial behavior and conscientiousness in females, and autonomy and assertiveness in males 73 .
A larger number of close friends and a high level of group vivacity were strongly correlated with expected cyber-aggressive responses.Participants who perceived their friend groups as larger and expected more lively exchanges (texts, emoticons) in their simulated WhatsApp groups also anticipated more aggressive behavior in response to all group norms.This suggests that having more friends and frequent interactions may either simply statistically increase the likelihood of cyber-aggression or facilitate it through emotional contagion in highly active, larger groups, where emotions and behaviors, including aggression, spread from person to person 74 .This observation, while relatively new, deserves further study as it highlights the complex ways in which group dynamics influence aggressive behavior.It was crucial to control for these variables to determine the consistency of experimentally induced effects.www.nature.com/scientificreports/ We did not find a relationship between Internet use and cyber-aggression, suggesting that frequent daily online activity (personal and educational use), does not increase the likelihood of expecting cyber-aggression.This finding is consistent with research by Álvarez-Garcia et al. 26 , but contrary to other studies 5,75 .It is important to note that our sample consisted of slightly older individuals and had below average daily Internet usage time 76 .Also, in contrast to Piccoli et al. 5 , we could not replicate significant association for ingroup prototypicality or ingroup identification with cyber-aggression.
Finally, we found no significant correlation between the fulfillment or subjective importance of developmental tasks and cyber-aggressive tendencies in the AYA sample.Although our approach was exploratory, this finding was unexpected given the presumed relevance of developmental milestones during this life stage 37,46,77 .The importance ratings indicated that 12 out of the 13 developmental tasks were important to the participants.The relatively low reliability of the measure may also partly explain the lack of significant results.The adapted DTQ-YA scale 51 , has been updated to meet current standards and developmental needs 46,42 but was not thoroughly validated prior to our study.Future studies could explore whether specific patterns or configurations of developmental task fulfillment (e.g., establishing a career without achieving a stable partnership) are better at predicting cyber-aggression.This would require validation of the measurement instrument 78 .
This study has several limitations.Our experimental approach allowed us to investigate how group norms affect AYA's expectations of their friends' cyber-aggression in WhatsApp groups in a controlled setting.However, our results may not fully capture the complex nature of group norms in everyday situations.The evaluation of cyber-aggression is based on hypothetical WhatsApp scenarios, which may not accurately reflect actual group dynamics and AYA reactions in real-life.We did not test if the induced group norms (funny/mocking, aggressive/insulting, or friendly/compassionate interaction tones) were perceived as intended 5 .Future studies should include a manipulation check for this issue.
Further limitations apply to the dependent variable 'expected cyber-aggressive reactions' .To avoid social desirability and self-categorization biases, participants rated their close friends' reactions rather than their own.While well-founded, this method has interpretability and generalizability limitations.Specifically, it assumes that individuals' perceptions of their friends' behavior reflect their own, which may not always be accurate.Research on the third-person effect suggests that individuals believe that others are more influenced by media messages than themselves, potentially skewing the data toward negative perceptions 79,80 .However, previous research (e.g., social norms approach to substance abuse prevention 81 ) suggests that the tolerability of cyber-aggression within one's own group has some impact on the respondents' actual and future behavior.Survey research shows that quantitative, evaluative estimations are influenced by unconscious desires that reflect, at least in part, the evaluator's cognitive style 82 .
Another limitation of the dependent variable is its simplification to a binary format.Initially, conformity to cyber-aggression was measured using multiple items (e.g., forwarding a photo, sending a nauseous emoji, writing an insulting comment) as counting variables.For practical reasons and to facilitate analysis, it was later simplified to a binary format, potentially limiting response variance.While a multilevel Poisson regression model might have been more suitable due to its ability to handle count data 83 , our study focused on testing two experimental factors and accounting for potential confounding influences, rather than making precise quantitative predictions.For purposes of the current study, distinguishing between any expected aggressive tendencies (as the lowest threshold) and no expected aggression was sufficient.However, future research should examine cyber-aggression in more detail for a more nuanced understanding.Lastly, although our research had a relatively large sample size, caution should be excercised when generalizing these results to AYA in different educational, cultural, and social contexts.
Overall, our findings indicate the importance of social dynamics, particularly group norms, in anticipating aggressive tendencies in WhatsApp groups.Although experimental effects may be smaller than in real-life situations, our results show that group norms are crucial in predicting and preventing cyber-aggression.Our analyses support a strong relationship between group norms in messaging apps and conformity behavior in cyber-aggressive scenarios.Altering these norms could reduce cyber-aggressive tendencies and increase prosocial actions 84 .As Pinho et al. argue, strategies that highlight actual group norms are essential for strengthening positive norms or reducing negative ones 84 .Our results underline the need for intervention strategies or educational trainings to help AYA develop prosocial and non-aggressive norms, fostering netiquette, and reduce negative peer-to-peer interactions.These findings could enhance current approaches 12 and improve the current care.Our realistic WhatsApp screenshots (stimulus material) could further be used in gamified approaches that have been effective in serious games like 'Bad News' for other parts of digital competence, such as fake news detection 85 .

Figure 1 .
Figure 1.Examples of WhatsApp vignette scenarios with different group norms (from left to right: funny, aggressive, friendly).Participants were either presented with fictive group chats composed of friends (as displayed here) or fellow school/university students (as indicated in the WhatsApp chat group name).The pixelated photo is described as showing a person with slipped clothes while doing sports.The vignette scenarios have been translated from German into English.

Figure 2 .
Figure 2. Exploratory predictors in the multiple logistic regression of expected cyber-aggression.The forest plot displays odds ratios and 95% confidence intervals on a log-scale.Significant predictors are indicated by black lines, while non-significant predictors are indicated by grey lines.Reference categories are indexed in Table3.
14:17231 | https://doi.org/10.1038/s41598-024-67915-9 Inspired by the Developmental Task Questionnaire for Young Adults (DTQ-YA) by Seiffge-Krenke 51 , a list of 13 items asking to what extent the respondent would rate a particular developmental task (e.g., entering the workforce, being part of a peer group, maintaining a stable relationship) as accomplished ('I have not yet achieved', 'I have begun', 'I have already achieved'), and how much they would rate the same list as important ('not important to me', 'somewhat important to me', 'very important to me').Based on focus group discussions

Table 1 .
Descriptive statistics of the sample.a Group vivacity.

Table 2 .
Results of the confirmatory model for cyber-aggressive reactions as predicted by type of group chat and group norm.Reference categories: a fellow student group, b friendly group norm.